How does AI help Spotify in Picking up your Next Tune?

Why Data is the magic Ingredient for Audio/Video Streaming Success
With millions and millions of users listening to music every minute of the day – brands like Spotify accumulate a mountain of implicit customer data comprised of song preferences, keyword preferences, playlist data, geographic location of listeners, most used devices and more. Spotify uses a combination of different data aggregation and sorting methods to create their unique and powerful recommendation model – which is powered by Machine Learning.
Data drives decisions across each and every department at Spotify. This data is used to train Spotify algorithms which hypothesize relevant insights both from content on the platform and from online conversations about music and artists – as well as from customer data and use this to enhance the user experience.
For instance, Discover Weekly – which has reached 40 million people in the first year it was introduced. On every Monday, each and every users are presented with a customized list of thirty songs. And the recommended playlist comprises tracks – which user might have not heard before but the recommendations are generated based on the user’s search history pattern and potential music recommendations. Machine learning enables the recommendations to improve over a period of time. Not only it keeps users returning but also enables greater exposure for artists – who users might not even search for organically.